Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains

Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains

Abstract Bayesian optimization (BO) has been leveraged for guiding autonomous and high-throughput experiments in materials science. However, few have evaluated the efficiency of BO across a broad range of experimental materials domains. In this work, we quantify the performance of BO with a collecti...

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Bibliographic Details
Main Authors: Qiaohao Liang, Aldair E. Gongora, Zekun Ren, Armi Tiihonen, Zhe Liu, Shijing Sun, James R. Deneault, Daniil Bash, Flore Mekki-Berrada, Saif A. Khan, Kedar Hippalgaonkar, Benji Maruyama, Keith A. Brown, John Fisher III, Tonio Buonassisi
Format: article
Language:EN
Published: Nature Portfolio 2021
Subjects:
Materials of engineering and construction. Mechanics of materials
TA401-492
Computer software
QA76.75-76.765
Online Access:https://doaj.org/article/bfc6d1e9f14b4b8bbc093f32c3360b8c
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https://doaj.org/article/bfc6d1e9f14b4b8bbc093f32c3360b8c

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